Land use and land cover (LULC) classification is known to be one of the most widely used indicators environmental change degradation all over world. There are various algorithms methods for LULC classification, whereby reliability maps presents principal concern. The study focused on evaluation accuracies produced from original bands Sentinel-2 imageries together with Normalized Difference Vege...

In this paper, we report a geospatial assessment of the selected mine sites in the Plateau State, Nigeria. The aim of this work is to determine the impact of mining on the terrain as well as the Land Use/Land Cover (LULC) of the host communities. The Shuttle Radar Topographic Mission (SRTM) is used for the terrain mapping. The derived impact of mining on LULC between 1975 and 2014 is determined...

Abstract The Accra Metropolis of Ghana has experienced rapid urban expansion over the past decades. Agricultural and forestlands have been transformed into urban/built-up areas. This study analysed its relationship with temperature from 1986 to 2022. Multi-source datasets such as remote sensing (RS) other ancillary data were utilised. Land use land cover (LULC) maps produced employing random fo...

Land use/land cover (LULC) change has been one of the main drivers landscape changes in Mediterranean region where there an increase forested lands. LULC not only changed forest stand structure and composition but also resulted available combustible fuels. There have few studies on fuel accumulation relation to LULC, however, no study explaining quantitatively relationship between canopy load r...

Microtubules display a wide diversity of roles in the living cell. Most of these involve the specific interactions of microtubule-associated proteins (MAPs) with tubulin. This is clearly seen at the level of motility of cell organelles, where MAPs with mechanochemical roles mediate the interaction between microtubules and organelles to facilitate their intracellular movements (Paschal et al., 1...

Land Use and Cover (LULC) changes are major global environmental issues, affecting ecological systems, climate change, biodiversity. The Chure region of the Sarlahi district in Nepal is a critical zone that has experienced significant LULC recent years. In this study, our aim was to apply machine learning approach detect using Google Earth Engine (GEE) Random Forest classifier. We utilized Land...

Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, management of the Earth. With advent cloud computing platforms, time series feature extraction techniques, machine learning classifiers, new opportunities arising in more accurate large-scale LULC mapping. In this study, we aimed at finding out how two compositio...

The large-scale pixel-based land use/land cover classification is a challenging task, which depends on many circumstances. This study aims to create LULC maps with the nomenclature of Coordination Information Environment (CORINE) Land Cover (CLC) for years when CLC databases are not available. Furthermore, testing predicted use changes in last 30 Hungary. Histogram-based gradient boosting tree ...

Land use and land cover (LULC) mapping can be of great help in changing decisions, but accurate LULC categories is challenging, especially semi-arid areas with extensive farming systems seasonal vegetation phenology. Machine learning algorithms are now widely used for because they provide analytical capabilities classification. However, the machine to improve classification performance still be...

BACKGROUND The estimation of forest biomass changes due to land-use change is of significant importance for estimates of the global carbon budget. The accuracy of biomass density maps depends on the availability of reliable allometric models used in combination with data derived from satellites images and forest inventory data. To reduce the uncertainty in estimates of carbon emissions resultin...